inst/shiny/ui_04_batchcorrect.R
In compbiomed/singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data
shinyPanelBatchcorrect <- fluidPage(
includeCSS('styles.CSS'),
tags$script("Shiny.addCustomMessageHandler('close_dropDownBC', function(x){
$('html').click();
});"),
h1("Normalization & Batch Correction"),
tabsetPanel(
tabPanel(
"Normalization", fluid = TRUE,
fluidRow(
column(
width = 4,
fluidRow(
column(
width = 12,
panel(
heading = "Normalization Options",
h5(tags$a(href = paste0(docs.artPath, "cnsl_normalization.html"),
"(help)", target = "_blank")),
selectInput(
inputId = "normalizeAssayMethodSelect",
label = "Select normalization method: ",
choices = c("Seurat - LogNormalize" = "LogNormalize",
"Seurat - CLR" = "CLR",
"Seurat - RC" = "RC",
"Seurat - SCTransform" = "SCTransform",
"Scater - LogNormCounts" = "logNormCounts",
"Scater - CPM" = "CPM",
"Scanpy - NormalizeTotal" = "NormalizeTotal",
"Custom Normalization" = "custom")
)
)
)
)
),
column(
width = 8,
fluidRow(
column(
width = 12,
panel(
heading = "Options",
fluidRow(
column(6, style='border-right:1px solid; border-color:#EDEDED;',
conditionalPanel(
condition = "input.normalizeAssayMethodSelect != 'custom'",
selectizeInput(
inputId = "normalizeAssaySelect",
label = "Select input matrix:",
choices = NULL,
selected = NULL,
multiple = FALSE,
options = NULL),
#uiOutput("normalizeAssaySelect"),
conditionalPanel(
condition = "input.normalizeAssayMethodSelect == 'LogNormalize'
|| input.normalizeAssayMethodSelect == 'CLR'
|| input.normalizeAssayMethodSelect == 'RC'",
numericInput(
inputId = "normalizationScaleFactor",
label = "Set scaling factor: ",
value = "10000"
)
),
textInput(
inputId = "normalizeAssayOutname",
label = "Assay Name:",
value = "SeuratLogNormalize"
),
awesomeCheckbox(
inputId = "normalizationScale",
label = "Scale data after normalization?",
value = FALSE
),
conditionalPanel(
condition = "input.normalizationScale == true",
awesomeCheckbox(
inputId = "normalizationTrim",
label = "Trim data after scale?",
value = TRUE
),
conditionalPanel(
condition = "input.normalizationTrim == true",
numericInput(
inputId = "normalizationTrimUpper",
label = "Upper trim value:",
value = 10
),
numericInput(
inputId = "normalizationTrimLower",
label = "Lower trim value:",
value = -10
)
)
)
),
conditionalPanel(
condition = "input.normalizeAssayMethodSelect == 'custom'",
h5("Assay Options:"),
selectizeInput(
inputId = "modifyAssaySelect",
label = "Select input matrix:",
choices = NULL,
selected = NULL,
multiple = FALSE,
options = NULL),
#uiOutput("modifyAssaySelect"),
textInput("modifyAssayOutname", "Assay Name",
value = "customNormalizedAssay"),
tags$hr(),
h5("Select Options:"),
awesomeCheckbox(
inputId = "customNormalizeOptionsNormalize",
label = "Normalize",
value = FALSE
),
conditionalPanel(
condition = "input.customNormalizeOptionsNormalize == true",
h5("Normalize Options:"),
selectInput(
inputId = "customNormalizeAssayMethodSelect",
label = "Select normalization method: ",
choices = c("Seurat - LogNormalize" = "LogNormalize",
"Seurat - CLR" = "CLR",
"Seurat - RC" = "RC",
"Seurat - SCTransform" = "SCTransform",
"Scater - LogNormCounts" = "logNormCounts",
"Scanpy - NormalizeTotal" = "NormalizeTotal",
"Scater - CPM" = "CPM")
)
),
conditionalPanel(
condition = "!(input.customNormalizeOptionsNormalize == true
&& (input.customNormalizeAssayMethodSelect == 'LogNormalize'
|| input.customNormalizeAssayMethodSelect == 'CLR'
|| input.customNormalizeAssayMethodSelect == 'SCTransform'
|| input.customNormalizeAssayMethodSelect == 'logNormCounts'))",
awesomeCheckbox(
inputId = "customNormalizeOptionsTransform",
label = "Transform",
value = FALSE
)
),
conditionalPanel(
condition = "input.customNormalizeOptionsTransform == true",
h5("Transformation Options:"),
selectInput(
inputId = "customNormalizeTransformOptions",
label = "Select transformation method:",
choices = c("Log2" = "log2",
"Log1p" = "log1p",
"Sqrt" = "sqrt")
)
),
awesomeCheckbox(
inputId = "customNormalizeOptionsPsuedocounts",
label = "Psuedocounts",
value = FALSE
),
conditionalPanel(
condition = "input.customNormalizeOptionsPsuedocounts == true",
h5("Pseudocounts Options:"),
conditionalPanel(
condition = "input.customNormalizeOptionsNormalize == true",
awesomeCheckbox(
inputId = "customNormalizePseudoOptionsBefore",
label = "before normalization",
value = FALSE
),
conditionalPanel(
condition = "input.customNormalizePseudoOptionsBefore == true",
numericInput(
inputId = "customNormalizePseudoValueBefore",
label = "Enter a pseudovalue to add:",
value = 1,
min = 1
)
)
),
conditionalPanel(
condition = "input.customNormalizeOptionsTransform == true",
awesomeCheckbox(
inputId = "customNormalizePseudoOptionsAfter",
label = "before transformation",
value = FALSE
),
conditionalPanel(
condition = "input.customNormalizePseudoOptionsAfter == true",
numericInput(
inputId = "customNormalizePseudoValueAfter",
label = "Enter a pseudovalue to add:",
value = 1,
min = 1
)
)
),
conditionalPanel(
condition = "input.customNormalizeOptionsNormalize == false
&& input.customNormalizeOptionsTransform == false",
h6("To add a pseudovalue, must select 'Normalize' or 'Transform' options!")
)
),
awesomeCheckbox(
inputId = "customNormalizeOptionsScale",
label = "Scale",
value = FALSE
),
conditionalPanel(
condition = "input.customNormalizeOptionsScale == true",
h5("Scale Options:"),
selectInput(
inputId = "customNormalizeScaleOptions",
label = "Select scaling method:",
choices = c("Z.Score" = "zscore")
)
),
awesomeCheckbox(
inputId = "customNormalizeOptionsTrim",
label = "Trim",
value = FALSE
),
conditionalPanel(
condition = "input.customNormalizeOptionsTrim == true",
h5("Trim Options:"),
numericInput(
inputId = "trimUpperValueAssay",
label = "Specify upper trim value",
value = 10
),
numericInput(
inputId = "trimLowerValueAssay",
label = "Specify lower trim value",
value = -10
)
)
)
),
column(6,
h4("Selected Options:"),
conditionalPanel(
condition = "input.normalizeAssayMethodSelect != 'custom'",
uiOutput("normalizationNormalizeSelectedMethodUI")
),
conditionalPanel(
condition = "(input.customNormalizeOptionsNormalize == true
&& input.normalizeAssayMethodSelect == 'custom')",
h5("Normalize")
),
conditionalPanel(
condition = "input.customNormalizeOptionsTransform == true
&& input.normalizeAssayMethodSelect == 'custom'",
h5("Transform")
),
conditionalPanel(
condition = "input.customNormalizeOptionsScale == true
&& input.normalizeAssayMethodSelect == 'custom'",
h5("Scale")
),
conditionalPanel(
condition = "input.customNormalizeOptionsPsuedocounts == true
&& input.normalizeAssayMethodSelect == 'custom'",
h5("Pseudocounts")
),
conditionalPanel(
condition = "input.customNormalizeOptionsTrim == true
&& input.normalizeAssayMethodSelect == 'custom'",
h5("Trim")
),
tags$hr(),
h4("Output Data Type:"),
uiOutput("normalizationDataTagUI")
)
),
fluidRow(
tags$hr(),
column(12,
conditionalPanel(
condition = "input.normalizeAssayMethodSelect != 'custom'",
div(style = "display:inline-block; float:right", actionButton("normalizeAssay", "Run")),
),
conditionalPanel(
condition = "input.normalizeAssayMethodSelect == 'custom'",
div(style = "display:inline-block; float:right", actionButton("modifyAssay", "Run"))
)
)
)
)
)
)
)
)
),
tabPanel(
"Batch Correction",
sidebarLayout(
sidebarPanel(
h3("Parameters"),
h5(tags$a(href = paste0(docs.artPath, "batch_correction.html"),
"(help)", target = "_blank")),
selectInput('batchCorrMethods', "Select Batch Correction Method:",
c("MNN", "ComBatSeq", "FastMNN", "Limma", #"BBKNN", "Harmony", "LIGER",
"scanorama", "scMerge", "Seurat3 Integration",
"ZINBWaVE")),
selectizeInput(
inputId = "batchCorrAssay",
label = "Select input matrix:",
choices = NULL,
selected = NULL,
multiple = FALSE,
options = NULL),
#uiOutput("batchCorrAssay"),
selectInput("batchCorrVar", "Select Batch Annotation:", clusterChoice),
# BBKNN ####
conditionalPanel(
condition = "input.batchCorrMethods == 'BBKNN'",
h5(tags$a(href = paste0(docs.base, "reference/runBBKNN.html"),
"(help for BBKNN)", target = "_blank")),
numericInput("BBKNNNComp", label = "Number of output dimension:",
value = 50L, min = 2, max = 100000, step = 1),
textInput("BBKNNSaveReddim", "ReducedDim Name to Use:",
value = "BBKNN"),
actionButton("BBKNNRun", "Run")
),
# ComBatSeq ####
conditionalPanel(
condition = "input.batchCorrMethods == 'ComBatSeq'",
h5(tags$a(href = paste0(docs.base, "reference/runComBatSeq.html"),
"(help for ComBatSeq)", target = "_blank")),
radioButtons("combatKnownCT", "Have known cell type variable?",
choices = c("Yes", "No")),
conditionalPanel(
condition = "input.combatKnownCT == 'Yes'",
selectInput("combatCond", "Select Condition of Covariance:",
clusterChoice),
),
conditionalPanel(
condition = "input.combatKnownCT == 'No'",
radioButtons("combatCTBalance", "Are cell types balanced?",
choices = c("Yes", "No")),
conditionalPanel(
condition = "input.combatCTBalance == 'No'",
p("Will estimate surrogate variables and use them as an empirical control"),
)
),
selectInput("combatBioCond", "Select Biology Condition:",
clusterChoice),
checkboxInput("combatShrink",
"Apply shrinkage on parameter estimation",
value = FALSE),
conditionalPanel(
condition = "input.combatShrink == true",
numericInput("combatNGene",
"Number of random genes to use in empirical Bayes estimation",
value = NULL)
),
checkboxInput("combatShrinkDisp",
"Apply shrinkage on dispersion",
value = FALSE),
textInput("combatSaveAssay", "Assay Name to Use:", value = "ComBatSeq"),
actionButton("combatRun", "Run")
),
# FastMNN ####
conditionalPanel(
condition = "input.batchCorrMethods == 'FastMNN'",
h5(tags$a(href = paste0(docs.base, "reference/runFastMNN.html"),
"(help for fastMNN)", target = "_blank")),
checkboxInput('FastMNNPcInput', "Use low-dimension input instead",
value = FALSE),
conditionalPanel(
condition = 'input.FastMNNPcInput == true',
selectInput('FastMNNReddim', "Select Reduced dimension:",
currreddim)
),
textInput("FastMNNSaveReddim", "ReducedDim Name to Use:",
value = "FastMNN"),
actionButton("FastMNNRun", "Run")
),
# # Harmony ####
# conditionalPanel(
# condition = "input.batchCorrMethods == 'Harmony'",
# h5(tags$a(href = paste0(docs.base, "reference/runHarmony.html"),
# "(help for Harmony)", target = "_blank")),
# checkboxInput('HarmonyPcInput', "Use low-dimension input instead",
# value = FALSE),
# conditionalPanel(
# condition = 'input.HarmonyPcInput == true',
# selectInput('HarmonyReddim', "Select Reduced dimension:",
# currreddim)
# ),
# numericInput("HarmonyNComp", label = "Number of output dimension:",
# value = 50L, min = 2, max = 100000, step = 1),
# textInput("HarmonyTheta", "Theta value", value = '5',
# placeholder = "Type a number"),
# numericInput("HarmonyNIter", "Number of iteration",
# value = 10L, min = 1, step = 1),
# textInput("HarmonySaveReddim", "ReducedDim Name to Use:",
# value = "Harmony"),
# withBusyIndicatorUI(actionButton("HarmonyRun", "Run"))
# ),
# # LIGER ####
# conditionalPanel(
# condition = "input.batchCorrMethods == 'LIGER'",
# h5(tags$a(href = paste0(docs.base, "reference/runLIGER.html"),
# "(help for LIGER)", target = "_blank")),
# numericInput("ligerNComp", label = "Number of output dimension:",
# value = 20L, min = 2, max = 100000, step = 1),
# numericInput("ligerLambda", label = "Lambda:",
# value = 5.0, min = 0, max = 100000, step = 0.1),
# numericInput("ligerResolution", label = "Resolution:",
# value = 1.0, min = 0, max = 100000, step = 0.1),
# textInput("ligerSaveReddim", "ReducedDim Name to Save:",
# value = "LIGER"),
# withBusyIndicatorUI(actionButton("ligerRun", "Run"))
# ),
# Limma ####
conditionalPanel(
condition = "input.batchCorrMethods == 'Limma'",
h5(tags$a(href = paste0(docs.base, "reference/runLimmaBC.html"),
"(help for Limma)", target = "_blank")),
textInput("limmaSaveAssay", "Assay Name to Use:", value = "LIMMA"),
actionButton("limmaRun", "Run")
),
# MNN ####
conditionalPanel(
condition = "input.batchCorrMethods == 'MNN'",
h5(tags$a(href = paste0(docs.base, "reference/runMNNCorrect.html"),
"(help for MNN)", target = "_blank")),
numericInput('MNNK', 'K value',
value = 20, min = 1, step = 1),
numericInput("MNNSigma", 'Sigma value', value = 0.1),
textInput("MNNSaveAssay", "Assay Name to Use:", value = "MNN"),
actionButton("MNNRun", "Run")
),
# scanorama ####
conditionalPanel(
condition = "input.batchCorrMethods == 'scanorama'",
h5(tags$a(href = paste0(docs.base, "reference/runSCANORAMA.html"),
"(help for Scanorama)", target = "_blank")),
numericInput('scnrmSIGMA', 'Sigma value', 15),
numericInput('scnrmALPHA', 'Alpha value', 0.1),
numericInput('scnrmKNN', 'KNN value', 20, min = 1, step = 1),
textInput("scnrmSaveAssay", "Assay Name to Use:",
value = "SCANORAMA"),
actionButton("scnrmRun", "Run")
),
# scMerge ####
conditionalPanel(
condition = "input.batchCorrMethods == 'scMerge'",
h5(tags$a(href = paste0(docs.base, "reference/runSCMerge.html"),
"(help for scMerge)", target = "_blank")),
## SEG options
radioButtons('scMergeSEGOpt', "Choose Stable Expressed Gene (SEG) seg",
choiceNames = c('Automatically identify',
'Use pre-computed dataset',
'Customized list'),
choiceValues = c(1, 2, 3)),
conditionalPanel(
condition = 'input.scMergeSEGOpt == 2',
radioButtons('scMergeSEGSpecies', "The species of SEG:",
choices = c('human', 'mouse'))
),
conditionalPanel(
condition = 'input.scMergeSEGOpt == 3',
textAreaInput("scMergeSEGCustom", "Use customized SEG:",
placeholder = "paste them here.\nOne per line with no symbol separator.")
),
checkboxInput("scMergeAutoKmk", "Automatically detect Kmeans-K",
value = TRUE),
conditionalPanel(
condition = "input.scMergeAutoKmk == false",
uiOutput('scMergeNBatch'),
textInput('scMergeUserKmk', label = NULL)
),
selectInput("scMergeCT", "Cell Type Annotation:", clusterChoice),
textInput("scMergeSaveAssay", "Assay Name to Use:",
value = "scMerge"),
actionButton("scMergeRun", "Run")
),
# Seurat3 Integration ####
conditionalPanel(
condition = "input.batchCorrMethods == 'Seurat3 Integration'",
h5(tags$a(href = paste0(docs.base, "reference/runSeuratIntegration.html"),
"(help for Seurat Integration)", target = "_blank")),
uiOutput('Srt3IntNAnchUI'),
textInput("Srt3IntSaveAssay", "Assay Name to Use:",
value = "Seurat3Int"),
actionButton("Srt3IntRun", "Run")
),
# ZINBWaVE ####
conditionalPanel(
condition = "input.batchCorrMethods == 'ZINBWaVE'",
h5(tags$a(href = paste0(docs.base, "reference/runZINBWaVE.html"),
"(help for ZINBWaVE)", target = "_blank")),
span("Test on small example not passed yet, don't run.",
style = 'color:red;'),
uiOutput('zinbwaveNHvgUI'),
uiOutput('zinbwaveEpsUI'),
numericInput('zinbwaveNIter', 'Max number of iteration:',
value = 10, step = 1),
numericInput('zinbwaveNComp', 'Number of dimensions to output:',
value = 50, min = 2, step = 1),
textInput("zinbwaveSaveReddim", "ReducedDim Name to Use:",
value = "ZINBWaVE"),
actionButton("zinbwaveRun", "Run")
),
uiOutput("batchCorrStatus")
),
mainPanel(
fluidRow(
class = "batchVarPlotRow",
panel(
heading = "Visualization",
fluidRow(
column(
width = 4,
dropdown(
fluidRow(
column(
width = 12,
fluidRow(actionBttn(inputId = "closeDropDownBC", label = NULL, style = "simple", color = "danger", icon = icon("times"), size = "xs"), align = "right"),
h5(tags$a(href = "https://compbiomed.github.io/sctk_docs/articles/batch_correction.html#visualization",
"(What are plotted?)", target = "_blank")),
selectizeInput(
inputId = "batchCheckOrigAssay",
label = "Select input matrix:",
choices = NULL,
selected = NULL,
multiple = FALSE,
options = NULL),
#uiOutput("batchCheckOrigAssay"),
#selectInput("batchCheckOrigAssay", "Original Assay:", currassays),
selectInput("batchCheckVar", "Batch Annotation:", clusterChoice),
selectInput("batchCheckCond", "Additional Condition (optional)",
clusterChoice),
p("Only result generated in the current session will be presented. ",
style = "color:grey;"),
uiOutput("batchCheckResUI"),
withBusyIndicatorUI(
actionBttn(
inputId = "plotBatchCheck",
label = "Update",
style = "bordered",
color = "primary",
size = "sm"
)
)
)
),
inputId = "dropDownBC",
icon = icon("cog"),
status = "primary",
circle = FALSE,
inline = TRUE
)
),
column(
width = 7,
fluidRow(
h6(
"The top two plots shows the variance explained by the grouping of batches and user specified conditions, and the bottom two plots present the low dimension representation of the datasets. Please refer to our documentation for detatil."
),
align="center"
)
)
),
hr(),
br(),
column(
width = 6,
style='border-right: 1px solid #CCCCCC',
h4('Original Status'),
plotOutput('batchOriVars',
height = "300px", width = "300px"),
plotOutput('batchOriPCA',
height = "300px", width = "400px")
),
column(
width = 6,
h4("Corrected Status"),
plotOutput('batchCorrVars',
height = "300px", width = "300px"),
plotOutput('batchCorrReddim',
height = "300px", width = "400px")
),
)
)
)
)
)
),
nonLinearWorkflowUI(id = "nlw-nbc")
)
compbiomed/singleCellTK documentation built on Oct. 27, 2024, 3:26 a.m.
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shinyPanelBatchcorrect <- fluidPage(
includeCSS('styles.CSS'),
tags$script("Shiny.addCustomMessageHandler('close_dropDownBC', function(x){
$('html').click();
});"),
h1("Normalization & Batch Correction"),
tabsetPanel(
tabPanel(
"Normalization", fluid = TRUE,
fluidRow(
column(
width = 4,
fluidRow(
column(
width = 12,
panel(
heading = "Normalization Options",
h5(tags$a(href = paste0(docs.artPath, "cnsl_normalization.html"),
"(help)", target = "_blank")),
selectInput(
inputId = "normalizeAssayMethodSelect",
label = "Select normalization method: ",
choices = c("Seurat - LogNormalize" = "LogNormalize",
"Seurat - CLR" = "CLR",
"Seurat - RC" = "RC",
"Seurat - SCTransform" = "SCTransform",
"Scater - LogNormCounts" = "logNormCounts",
"Scater - CPM" = "CPM",
"Scanpy - NormalizeTotal" = "NormalizeTotal",
"Custom Normalization" = "custom")
)
)
)
)
),
column(
width = 8,
fluidRow(
column(
width = 12,
panel(
heading = "Options",
fluidRow(
column(6, style='border-right:1px solid; border-color:#EDEDED;',
conditionalPanel(
condition = "input.normalizeAssayMethodSelect != 'custom'",
selectizeInput(
inputId = "normalizeAssaySelect",
label = "Select input matrix:",
choices = NULL,
selected = NULL,
multiple = FALSE,
options = NULL),
#uiOutput("normalizeAssaySelect"),
conditionalPanel(
condition = "input.normalizeAssayMethodSelect == 'LogNormalize'
|| input.normalizeAssayMethodSelect == 'CLR'
|| input.normalizeAssayMethodSelect == 'RC'",
numericInput(
inputId = "normalizationScaleFactor",
label = "Set scaling factor: ",
value = "10000"
)
),
textInput(
inputId = "normalizeAssayOutname",
label = "Assay Name:",
value = "SeuratLogNormalize"
),
awesomeCheckbox(
inputId = "normalizationScale",
label = "Scale data after normalization?",
value = FALSE
),
conditionalPanel(
condition = "input.normalizationScale == true",
awesomeCheckbox(
inputId = "normalizationTrim",
label = "Trim data after scale?",
value = TRUE
),
conditionalPanel(
condition = "input.normalizationTrim == true",
numericInput(
inputId = "normalizationTrimUpper",
label = "Upper trim value:",
value = 10
),
numericInput(
inputId = "normalizationTrimLower",
label = "Lower trim value:",
value = -10
)
)
)
),
conditionalPanel(
condition = "input.normalizeAssayMethodSelect == 'custom'",
h5("Assay Options:"),
selectizeInput(
inputId = "modifyAssaySelect",
label = "Select input matrix:",
choices = NULL,
selected = NULL,
multiple = FALSE,
options = NULL),
#uiOutput("modifyAssaySelect"),
textInput("modifyAssayOutname", "Assay Name",
value = "customNormalizedAssay"),
tags$hr(),
h5("Select Options:"),
awesomeCheckbox(
inputId = "customNormalizeOptionsNormalize",
label = "Normalize",
value = FALSE
),
conditionalPanel(
condition = "input.customNormalizeOptionsNormalize == true",
h5("Normalize Options:"),
selectInput(
inputId = "customNormalizeAssayMethodSelect",
label = "Select normalization method: ",
choices = c("Seurat - LogNormalize" = "LogNormalize",
"Seurat - CLR" = "CLR",
"Seurat - RC" = "RC",
"Seurat - SCTransform" = "SCTransform",
"Scater - LogNormCounts" = "logNormCounts",
"Scanpy - NormalizeTotal" = "NormalizeTotal",
"Scater - CPM" = "CPM")
)
),
conditionalPanel(
condition = "!(input.customNormalizeOptionsNormalize == true
&& (input.customNormalizeAssayMethodSelect == 'LogNormalize'
|| input.customNormalizeAssayMethodSelect == 'CLR'
|| input.customNormalizeAssayMethodSelect == 'SCTransform'
|| input.customNormalizeAssayMethodSelect == 'logNormCounts'))",
awesomeCheckbox(
inputId = "customNormalizeOptionsTransform",
label = "Transform",
value = FALSE
)
),
conditionalPanel(
condition = "input.customNormalizeOptionsTransform == true",
h5("Transformation Options:"),
selectInput(
inputId = "customNormalizeTransformOptions",
label = "Select transformation method:",
choices = c("Log2" = "log2",
"Log1p" = "log1p",
"Sqrt" = "sqrt")
)
),
awesomeCheckbox(
inputId = "customNormalizeOptionsPsuedocounts",
label = "Psuedocounts",
value = FALSE
),
conditionalPanel(
condition = "input.customNormalizeOptionsPsuedocounts == true",
h5("Pseudocounts Options:"),
conditionalPanel(
condition = "input.customNormalizeOptionsNormalize == true",
awesomeCheckbox(
inputId = "customNormalizePseudoOptionsBefore",
label = "before normalization",
value = FALSE
),
conditionalPanel(
condition = "input.customNormalizePseudoOptionsBefore == true",
numericInput(
inputId = "customNormalizePseudoValueBefore",
label = "Enter a pseudovalue to add:",
value = 1,
min = 1
)
)
),
conditionalPanel(
condition = "input.customNormalizeOptionsTransform == true",
awesomeCheckbox(
inputId = "customNormalizePseudoOptionsAfter",
label = "before transformation",
value = FALSE
),
conditionalPanel(
condition = "input.customNormalizePseudoOptionsAfter == true",
numericInput(
inputId = "customNormalizePseudoValueAfter",
label = "Enter a pseudovalue to add:",
value = 1,
min = 1
)
)
),
conditionalPanel(
condition = "input.customNormalizeOptionsNormalize == false
&& input.customNormalizeOptionsTransform == false",
h6("To add a pseudovalue, must select 'Normalize' or 'Transform' options!")
)
),
awesomeCheckbox(
inputId = "customNormalizeOptionsScale",
label = "Scale",
value = FALSE
),
conditionalPanel(
condition = "input.customNormalizeOptionsScale == true",
h5("Scale Options:"),
selectInput(
inputId = "customNormalizeScaleOptions",
label = "Select scaling method:",
choices = c("Z.Score" = "zscore")
)
),
awesomeCheckbox(
inputId = "customNormalizeOptionsTrim",
label = "Trim",
value = FALSE
),
conditionalPanel(
condition = "input.customNormalizeOptionsTrim == true",
h5("Trim Options:"),
numericInput(
inputId = "trimUpperValueAssay",
label = "Specify upper trim value",
value = 10
),
numericInput(
inputId = "trimLowerValueAssay",
label = "Specify lower trim value",
value = -10
)
)
)
),
column(6,
h4("Selected Options:"),
conditionalPanel(
condition = "input.normalizeAssayMethodSelect != 'custom'",
uiOutput("normalizationNormalizeSelectedMethodUI")
),
conditionalPanel(
condition = "(input.customNormalizeOptionsNormalize == true
&& input.normalizeAssayMethodSelect == 'custom')",
h5("Normalize")
),
conditionalPanel(
condition = "input.customNormalizeOptionsTransform == true
&& input.normalizeAssayMethodSelect == 'custom'",
h5("Transform")
),
conditionalPanel(
condition = "input.customNormalizeOptionsScale == true
&& input.normalizeAssayMethodSelect == 'custom'",
h5("Scale")
),
conditionalPanel(
condition = "input.customNormalizeOptionsPsuedocounts == true
&& input.normalizeAssayMethodSelect == 'custom'",
h5("Pseudocounts")
),
conditionalPanel(
condition = "input.customNormalizeOptionsTrim == true
&& input.normalizeAssayMethodSelect == 'custom'",
h5("Trim")
),
tags$hr(),
h4("Output Data Type:"),
uiOutput("normalizationDataTagUI")
)
),
fluidRow(
tags$hr(),
column(12,
conditionalPanel(
condition = "input.normalizeAssayMethodSelect != 'custom'",
div(style = "display:inline-block; float:right", actionButton("normalizeAssay", "Run")),
),
conditionalPanel(
condition = "input.normalizeAssayMethodSelect == 'custom'",
div(style = "display:inline-block; float:right", actionButton("modifyAssay", "Run"))
)
)
)
)
)
)
)
)
),
tabPanel(
"Batch Correction",
sidebarLayout(
sidebarPanel(
h3("Parameters"),
h5(tags$a(href = paste0(docs.artPath, "batch_correction.html"),
"(help)", target = "_blank")),
selectInput('batchCorrMethods', "Select Batch Correction Method:",
c("MNN", "ComBatSeq", "FastMNN", "Limma", #"BBKNN", "Harmony", "LIGER",
"scanorama", "scMerge", "Seurat3 Integration",
"ZINBWaVE")),
selectizeInput(
inputId = "batchCorrAssay",
label = "Select input matrix:",
choices = NULL,
selected = NULL,
multiple = FALSE,
options = NULL),
#uiOutput("batchCorrAssay"),
selectInput("batchCorrVar", "Select Batch Annotation:", clusterChoice),
# BBKNN ####
conditionalPanel(
condition = "input.batchCorrMethods == 'BBKNN'",
h5(tags$a(href = paste0(docs.base, "reference/runBBKNN.html"),
"(help for BBKNN)", target = "_blank")),
numericInput("BBKNNNComp", label = "Number of output dimension:",
value = 50L, min = 2, max = 100000, step = 1),
textInput("BBKNNSaveReddim", "ReducedDim Name to Use:",
value = "BBKNN"),
actionButton("BBKNNRun", "Run")
),
# ComBatSeq ####
conditionalPanel(
condition = "input.batchCorrMethods == 'ComBatSeq'",
h5(tags$a(href = paste0(docs.base, "reference/runComBatSeq.html"),
"(help for ComBatSeq)", target = "_blank")),
radioButtons("combatKnownCT", "Have known cell type variable?",
choices = c("Yes", "No")),
conditionalPanel(
condition = "input.combatKnownCT == 'Yes'",
selectInput("combatCond", "Select Condition of Covariance:",
clusterChoice),
),
conditionalPanel(
condition = "input.combatKnownCT == 'No'",
radioButtons("combatCTBalance", "Are cell types balanced?",
choices = c("Yes", "No")),
conditionalPanel(
condition = "input.combatCTBalance == 'No'",
p("Will estimate surrogate variables and use them as an empirical control"),
)
),
selectInput("combatBioCond", "Select Biology Condition:",
clusterChoice),
checkboxInput("combatShrink",
"Apply shrinkage on parameter estimation",
value = FALSE),
conditionalPanel(
condition = "input.combatShrink == true",
numericInput("combatNGene",
"Number of random genes to use in empirical Bayes estimation",
value = NULL)
),
checkboxInput("combatShrinkDisp",
"Apply shrinkage on dispersion",
value = FALSE),
textInput("combatSaveAssay", "Assay Name to Use:", value = "ComBatSeq"),
actionButton("combatRun", "Run")
),
# FastMNN ####
conditionalPanel(
condition = "input.batchCorrMethods == 'FastMNN'",
h5(tags$a(href = paste0(docs.base, "reference/runFastMNN.html"),
"(help for fastMNN)", target = "_blank")),
checkboxInput('FastMNNPcInput', "Use low-dimension input instead",
value = FALSE),
conditionalPanel(
condition = 'input.FastMNNPcInput == true',
selectInput('FastMNNReddim', "Select Reduced dimension:",
currreddim)
),
textInput("FastMNNSaveReddim", "ReducedDim Name to Use:",
value = "FastMNN"),
actionButton("FastMNNRun", "Run")
),
# # Harmony ####
# conditionalPanel(
# condition = "input.batchCorrMethods == 'Harmony'",
# h5(tags$a(href = paste0(docs.base, "reference/runHarmony.html"),
# "(help for Harmony)", target = "_blank")),
# checkboxInput('HarmonyPcInput', "Use low-dimension input instead",
# value = FALSE),
# conditionalPanel(
# condition = 'input.HarmonyPcInput == true',
# selectInput('HarmonyReddim', "Select Reduced dimension:",
# currreddim)
# ),
# numericInput("HarmonyNComp", label = "Number of output dimension:",
# value = 50L, min = 2, max = 100000, step = 1),
# textInput("HarmonyTheta", "Theta value", value = '5',
# placeholder = "Type a number"),
# numericInput("HarmonyNIter", "Number of iteration",
# value = 10L, min = 1, step = 1),
# textInput("HarmonySaveReddim", "ReducedDim Name to Use:",
# value = "Harmony"),
# withBusyIndicatorUI(actionButton("HarmonyRun", "Run"))
# ),
# # LIGER ####
# conditionalPanel(
# condition = "input.batchCorrMethods == 'LIGER'",
# h5(tags$a(href = paste0(docs.base, "reference/runLIGER.html"),
# "(help for LIGER)", target = "_blank")),
# numericInput("ligerNComp", label = "Number of output dimension:",
# value = 20L, min = 2, max = 100000, step = 1),
# numericInput("ligerLambda", label = "Lambda:",
# value = 5.0, min = 0, max = 100000, step = 0.1),
# numericInput("ligerResolution", label = "Resolution:",
# value = 1.0, min = 0, max = 100000, step = 0.1),
# textInput("ligerSaveReddim", "ReducedDim Name to Save:",
# value = "LIGER"),
# withBusyIndicatorUI(actionButton("ligerRun", "Run"))
# ),
# Limma ####
conditionalPanel(
condition = "input.batchCorrMethods == 'Limma'",
h5(tags$a(href = paste0(docs.base, "reference/runLimmaBC.html"),
"(help for Limma)", target = "_blank")),
textInput("limmaSaveAssay", "Assay Name to Use:", value = "LIMMA"),
actionButton("limmaRun", "Run")
),
# MNN ####
conditionalPanel(
condition = "input.batchCorrMethods == 'MNN'",
h5(tags$a(href = paste0(docs.base, "reference/runMNNCorrect.html"),
"(help for MNN)", target = "_blank")),
numericInput('MNNK', 'K value',
value = 20, min = 1, step = 1),
numericInput("MNNSigma", 'Sigma value', value = 0.1),
textInput("MNNSaveAssay", "Assay Name to Use:", value = "MNN"),
actionButton("MNNRun", "Run")
),
# scanorama ####
conditionalPanel(
condition = "input.batchCorrMethods == 'scanorama'",
h5(tags$a(href = paste0(docs.base, "reference/runSCANORAMA.html"),
"(help for Scanorama)", target = "_blank")),
numericInput('scnrmSIGMA', 'Sigma value', 15),
numericInput('scnrmALPHA', 'Alpha value', 0.1),
numericInput('scnrmKNN', 'KNN value', 20, min = 1, step = 1),
textInput("scnrmSaveAssay", "Assay Name to Use:",
value = "SCANORAMA"),
actionButton("scnrmRun", "Run")
),
# scMerge ####
conditionalPanel(
condition = "input.batchCorrMethods == 'scMerge'",
h5(tags$a(href = paste0(docs.base, "reference/runSCMerge.html"),
"(help for scMerge)", target = "_blank")),
## SEG options
radioButtons('scMergeSEGOpt', "Choose Stable Expressed Gene (SEG) seg",
choiceNames = c('Automatically identify',
'Use pre-computed dataset',
'Customized list'),
choiceValues = c(1, 2, 3)),
conditionalPanel(
condition = 'input.scMergeSEGOpt == 2',
radioButtons('scMergeSEGSpecies', "The species of SEG:",
choices = c('human', 'mouse'))
),
conditionalPanel(
condition = 'input.scMergeSEGOpt == 3',
textAreaInput("scMergeSEGCustom", "Use customized SEG:",
placeholder = "paste them here.\nOne per line with no symbol separator.")
),
checkboxInput("scMergeAutoKmk", "Automatically detect Kmeans-K",
value = TRUE),
conditionalPanel(
condition = "input.scMergeAutoKmk == false",
uiOutput('scMergeNBatch'),
textInput('scMergeUserKmk', label = NULL)
),
selectInput("scMergeCT", "Cell Type Annotation:", clusterChoice),
textInput("scMergeSaveAssay", "Assay Name to Use:",
value = "scMerge"),
actionButton("scMergeRun", "Run")
),
# Seurat3 Integration ####
conditionalPanel(
condition = "input.batchCorrMethods == 'Seurat3 Integration'",
h5(tags$a(href = paste0(docs.base, "reference/runSeuratIntegration.html"),
"(help for Seurat Integration)", target = "_blank")),
uiOutput('Srt3IntNAnchUI'),
textInput("Srt3IntSaveAssay", "Assay Name to Use:",
value = "Seurat3Int"),
actionButton("Srt3IntRun", "Run")
),
# ZINBWaVE ####
conditionalPanel(
condition = "input.batchCorrMethods == 'ZINBWaVE'",
h5(tags$a(href = paste0(docs.base, "reference/runZINBWaVE.html"),
"(help for ZINBWaVE)", target = "_blank")),
span("Test on small example not passed yet, don't run.",
style = 'color:red;'),
uiOutput('zinbwaveNHvgUI'),
uiOutput('zinbwaveEpsUI'),
numericInput('zinbwaveNIter', 'Max number of iteration:',
value = 10, step = 1),
numericInput('zinbwaveNComp', 'Number of dimensions to output:',
value = 50, min = 2, step = 1),
textInput("zinbwaveSaveReddim", "ReducedDim Name to Use:",
value = "ZINBWaVE"),
actionButton("zinbwaveRun", "Run")
),
uiOutput("batchCorrStatus")
),
mainPanel(
fluidRow(
class = "batchVarPlotRow",
panel(
heading = "Visualization",
fluidRow(
column(
width = 4,
dropdown(
fluidRow(
column(
width = 12,
fluidRow(actionBttn(inputId = "closeDropDownBC", label = NULL, style = "simple", color = "danger", icon = icon("times"), size = "xs"), align = "right"),
h5(tags$a(href = "https://compbiomed.github.io/sctk_docs/articles/batch_correction.html#visualization",
"(What are plotted?)", target = "_blank")),
selectizeInput(
inputId = "batchCheckOrigAssay",
label = "Select input matrix:",
choices = NULL,
selected = NULL,
multiple = FALSE,
options = NULL),
#uiOutput("batchCheckOrigAssay"),
#selectInput("batchCheckOrigAssay", "Original Assay:", currassays),
selectInput("batchCheckVar", "Batch Annotation:", clusterChoice),
selectInput("batchCheckCond", "Additional Condition (optional)",
clusterChoice),
p("Only result generated in the current session will be presented. ",
style = "color:grey;"),
uiOutput("batchCheckResUI"),
withBusyIndicatorUI(
actionBttn(
inputId = "plotBatchCheck",
label = "Update",
style = "bordered",
color = "primary",
size = "sm"
)
)
)
),
inputId = "dropDownBC",
icon = icon("cog"),
status = "primary",
circle = FALSE,
inline = TRUE
)
),
column(
width = 7,
fluidRow(
h6(
"The top two plots shows the variance explained by the grouping of batches and user specified conditions, and the bottom two plots present the low dimension representation of the datasets. Please refer to our documentation for detatil."
),
align="center"
)
)
),
hr(),
br(),
column(
width = 6,
style='border-right: 1px solid #CCCCCC',
h4('Original Status'),
plotOutput('batchOriVars',
height = "300px", width = "300px"),
plotOutput('batchOriPCA',
height = "300px", width = "400px")
),
column(
width = 6,
h4("Corrected Status"),
plotOutput('batchCorrVars',
height = "300px", width = "300px"),
plotOutput('batchCorrReddim',
height = "300px", width = "400px")
),
)
)
)
)
)
),
nonLinearWorkflowUI(id = "nlw-nbc")
)
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